KA129001 Practical English I

Hiroshima University Syllabus

Japanese

Academic Year 2024Year School/Graduate School School of Informatics and Data Science

Lecture Code KA129001 Subject Classification Specialized Education

Subject Name 実用英語I

Subject Name
（Katakana）ジツヨウエイゴ１

Subject Name in
English Practical English I

Instructor TING HIAN ANN

Instructor
(Katakana) ティン　ヒェン　アン

Campus Higashi-Hiroshima Semester/Term 3rd-Year,  First Semester,  1Term

Days, Periods, and Classrooms (1T) Tues5-8：ENG 106

Lesson Style Seminar Lesson Style
(More Details) 　

Each lecture is followed by a tutorial as a set of two lessons. Lectures are exclusively in English. There will be simple quizzes and exercises to keep students alert. Each week, before 1 PM, the in-class exercises must be submitted.  Solutions for each homework assignment are to be typeset in English with latex and submit as a pair of latex source file and the resulting pdf file.

Credits 1.0 Class Hours/Week 　 Language of Instruction E : English

Course Level 4 : Undergraduate Advanced

Course Area（Area） 25 : Science and Technology

Course Area（Discipline） 01 : Mathematics/Statistics

Eligible Students

Keywords

Special Subject for Teacher Education 　 Special Subject 　

Class Status
within Educational
Program
(Applicable only to targeted subjects for undergraduate students) ・D1. Knowledge and skills required for understanding the theoretical system of statistics and data analysis, and for precisely and efficiently analyzing qualitative/quantitative information in big data.
・A. Skills related to the development of an information infrastructure, information processing techniques, and technology for producing new added value through data analysis.
・B. Ability to identify and solve new problems on their own by quantitative and logical thinking based on data, diverse perspectives, and advanced skills for information processing and analysis.
Criterion referenced
Evaluation
(Applicable only to targeted subjects for undergraduate students) Computer Science Program
（Comprehensive Abilities）
・C2. English conversation, reading, and writing skills are necessary for conducting research, good oral presentation skills, documentation skills for open discussion, and communication skills.

Data Science Program
（Comprehensive Abilities）
・C2. English conversation, reading, and writing skills are necessary for conducting research, good oral presentation skills, documentation skills for open discussion, and communication skills.

Intelligence Science Program
（Comprehensive Abilities）
・C2. English conversation, reading, and writing skills are necessary for conducting research, good oral presentation skills, documentation skills for open discussion, and communication skills.

Class Objectives
/Class Outline Rapid globalization brings about a need to develop a practical proficiency in English that is applicable internationally. As a foundation to attain the required English proficiency, this course is about learning a wide range of vocabulary so as to be able to read and comprehend, in English, textbooks, articles, and manuals related to informatics and data science. The ability to express such academic contents in English will be developed. Moreover, the basic communication capability for various areas related to informatics and data science will be cultivated. This course will also be beneficial to students in helping them to acquire the ability to learn English on their own.

Class Schedule lesson1 Vector and matrix: An introduction
lesson2 Tutorial (Exercise with LaTeX)
lesson3 Simultaneous system of first-order equations
lesson4 Tutorial (Exercise with LaTeX)
lesson5 Permutation and determinant
lesson6 Tutorial (Exercise with LaTeX)
lesson7 Linear space
lesson8 Tutorial (Exercise with LaTeX)
lesson9 Linear mapping
lesson10 Tutorial (Exercise with LaTeX)
lesson11 Inner product space
lesson12 Tutorial (Exercise with LaTeX)
lesson13 Eigenvalue and eigenvector
lesson14 Tutorial (Exercise with LaTeX)
lesson15 Summary

Each student's performance is evaluated based on the in-class exercises and homework assignment reports. No examination will be conducted.

Students will learn to apply linear algebra to appreciate machine learning.

Text/Reference
Books,etc. Online lecture notes
線形代数学 (linear algebra) by 栗田 (Kurita), 飯間 (Iima), and 河村 (Kawamura); edited by 久保 (kubo), 2017

PC or AV used in
Class,etc. 　

(More Details) Zoom
Lecture slides, notes, latex

Learning techniques to be incorporated 　

Suggestions on
Preparation and
Review It is important to take notes during the lesson.

Requirements Lecture notes and teaching materials will be uploaded to bb9 before the lesson begins.  It is important to go through the lecture notes and  homework questions before attending the weekly class.

Grading Method The credit will be evaluated based on individual reports and presentation. 60/100 point is the minimum requirement. The evaluation is based on (i) fundamental understanding of linear algebra in English, (ii) problem solving skill, (iii) English proficiency demonstrated in the report, (iv) logical steps that arrive at the solution, (v) aesthetic of the report.

Practical Experience

Summary of Practical Experience and Class Contents based on it

Message Develop the precious skills of writing beautiful formulas with latex

Other

Please fill in the class improvement questionnaire which is carried out on all classes.
Instructors will reflect on your feedback and utilize the information for improving their teaching.

Academic Year	2024Year	School/Graduate School	School of Informatics and Data Science
Lecture Code	KA129001	Subject Classification	Specialized Education
Subject Name	実用英語I
Subject Name （Katakana）	ジツヨウエイゴ１
Subject Name in English	Practical English I
Instructor	TING HIAN ANN
Instructor (Katakana)	ティン　ヒェン　アン
Campus	Higashi-Hiroshima	Semester/Term	3rd-Year, First Semester, 1Term
Days, Periods, and Classrooms	(1T) Tues5-8：ENG 106
Lesson Style	Seminar	Lesson Style (More Details)
Each lecture is followed by a tutorial as a set of two lessons. Lectures are exclusively in English. There will be simple quizzes and exercises to keep students alert. Each week, before 1 PM, the in-class exercises must be submitted. Solutions for each homework assignment are to be typeset in English with latex and submit as a pair of latex source file and the resulting pdf file.
Credits	1.0	Class Hours/Week		Language of Instruction	E : English
Course Level	4 : Undergraduate Advanced
Course Area（Area）	25 : Science and Technology
Course Area（Discipline）	01 : Mathematics/Statistics
Eligible Students
Keywords
Special Subject for Teacher Education		Special Subject
Class Status within Educational Program (Applicable only to targeted subjects for undergraduate students)	・D1. Knowledge and skills required for understanding the theoretical system of statistics and data analysis, and for precisely and efficiently analyzing qualitative/quantitative information in big data. ・A. Skills related to the development of an information infrastructure, information processing techniques, and technology for producing new added value through data analysis. ・B. Ability to identify and solve new problems on their own by quantitative and logical thinking based on data, diverse perspectives, and advanced skills for information processing and analysis.
Criterion referenced Evaluation (Applicable only to targeted subjects for undergraduate students)	Computer Science Program （Comprehensive Abilities）・C2. English conversation, reading, and writing skills are necessary for conducting research, good oral presentation skills, documentation skills for open discussion, and communication skills. Data Science Program （Comprehensive Abilities）・C2. English conversation, reading, and writing skills are necessary for conducting research, good oral presentation skills, documentation skills for open discussion, and communication skills. Intelligence Science Program （Comprehensive Abilities）・C2. English conversation, reading, and writing skills are necessary for conducting research, good oral presentation skills, documentation skills for open discussion, and communication skills.
Class Objectives /Class Outline	Rapid globalization brings about a need to develop a practical proficiency in English that is applicable internationally. As a foundation to attain the required English proficiency, this course is about learning a wide range of vocabulary so as to be able to read and comprehend, in English, textbooks, articles, and manuals related to informatics and data science. The ability to express such academic contents in English will be developed. Moreover, the basic communication capability for various areas related to informatics and data science will be cultivated. This course will also be beneficial to students in helping them to acquire the ability to learn English on their own.
Class Schedule	lesson1 Vector and matrix: An introduction lesson2 Tutorial (Exercise with LaTeX) lesson3 Simultaneous system of first-order equations lesson4 Tutorial (Exercise with LaTeX) lesson5 Permutation and determinant lesson6 Tutorial (Exercise with LaTeX) lesson7 Linear space lesson8 Tutorial (Exercise with LaTeX) lesson9 Linear mapping lesson10 Tutorial (Exercise with LaTeX) lesson11 Inner product space lesson12 Tutorial (Exercise with LaTeX) lesson13 Eigenvalue and eigenvector lesson14 Tutorial (Exercise with LaTeX) lesson15 Summary Each student's performance is evaluated based on the in-class exercises and homework assignment reports. No examination will be conducted. Students will learn to apply linear algebra to appreciate machine learning.
Text/Reference Books,etc.	Online lecture notes 線形代数学 (linear algebra) by 栗田 (Kurita), 飯間 (Iima), and 河村 (Kawamura); edited by 久保 (kubo), 2017
PC or AV used in Class,etc.
(More Details)	Zoom Lecture slides, notes, latex
Learning techniques to be incorporated
Suggestions on Preparation and Review	It is important to take notes during the lesson.
Requirements	Lecture notes and teaching materials will be uploaded to bb9 before the lesson begins. It is important to go through the lecture notes and homework questions before attending the weekly class.
Grading Method	The credit will be evaluated based on individual reports and presentation. 60/100 point is the minimum requirement. The evaluation is based on (i) fundamental understanding of linear algebra in English, (ii) problem solving skill, (iii) English proficiency demonstrated in the report, (iv) logical steps that arrive at the solution, (v) aesthetic of the report.
Practical Experience
Summary of Practical Experience and Class Contents based on it
Message	Develop the precious skills of writing beautiful formulas with latex
Other
Please fill in the class improvement questionnaire which is carried out on all classes. Instructors will reflect on your feedback and utilize the information for improving their teaching.

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